code: def test_split_dataset_list(self):
      n_sample, n_cols, n_pred, left_size, right_size = (
          100,
          2,
          1,
          0.2,
          0.8,
      )
      dataset = [
          np.random.sample((n_sample, n_cols)),
          np.random.sample((n_sample, n_pred)),
      ]
      dataset_left, dataset_right = split_dataset(
          dataset, left_size=left_size, right_size=right_size
      )
      self.assertEqual(
          int(dataset_left.cardinality()), int(n_sample * left_size)
      )
      self.assertEqual(
          int(dataset_right.cardinality()), int(n_sample * right_size)
      )
      self.assertEqual(
          [sample for sample in dataset_right][0][0].shape, (n_cols)
      )
  
      n_sample, n_cols, n_pred, left_size, right_size = (
          100,
          2,
          1,
          0.2,
          0.8,
      )
      dataset = [
          np.random.sample((n_sample, 100, n_cols)),
          np.random.sample((n_sample, n_pred)),
      ]
      dataset_left, dataset_right = split_dataset(
          dataset, left_size=left_size, right_size=right_size
      )
      self.assertEqual(
          int(dataset_left.cardinality()), int(n_sample * left_size)
      )
      self.assertEqual(
          int(dataset_right.cardinality()), int(n_sample * right_size)
      )
      self.assertEqual(
          [sample for sample in dataset_right][0][0].shape,
          (100, n_cols),
      )
  
      n_sample, n_cols, n_pred, left_size, right_size = (
          100,
          2,
          1,
          0.2,
          0.8,
      )
      dataset = [
          np.random.sample((n_sample, 10, 10, n_cols)),
          np.random.sample((n_sample, n_pred)),
      ]
      dataset_left, dataset_right = split_dataset(
          dataset, left_size=left_size, right_size=right_size
      )
      self.assertEqual(
          int(dataset_left.cardinality()), int(n_sample * left_size)
      )
      self.assertEqual(
          int(dataset_right.cardinality()), int(n_sample * right_size)
      )
      self.assertEqual(
          [sample for sample in dataset_right][0][0].shape,
          (10, 10, n_cols),
      )
  
      n_sample, n_cols, n_pred, left_size, right_size = (
          100,
          2,
          1,
          0.2,
          0.8,
      )
      dataset = [
          np.random.sample((n_sample, 100, 10, 30, n_cols)),
          np.random.sample((n_sample, n_pred)),
      ]
      dataset_left, dataset_right = split_dataset(
          dataset, left_size=left_size, right_size=right_size
      )
      self.assertEqual(
          int(dataset_left.cardinality()), int(n_sample * left_size)
      )
      self.assertEqual(
          int(dataset_right.cardinality()), int(n_sample * right_size)
      )
      self.assertEqual(
          [sample for sample in dataset_right][0][0].shape,
          (100, 10, 30, n_cols),
      )
  
file_path: keras/keras/utils/dataset_utils_test.py
imports: import numpy
  from keras.testing import test_case
  from keras.utils.dataset_utils import split_dataset
  from keras.utils.module_utils import tensorflow
label_desc: Use image node_id 1 for calling the DatasetUtilsTest obj's underlying member method code with example usage: obj.test_split_dataset_list() without return types